Perceptual Evaluation of Driving Scene Segmentation.

Human visual perception forms different levels of abstractions expressing the essential semantic components in the scene at different scales. For real-world applications such as driving scene perception, abstractions of both coarse-level, such as the spatial presence of the lead vehicle, and the fine-level, such as the words on a traffic sign, serve as important signals for driver's decision making. However, the granularities of perception required for levels of abstractions are generally different. While current computer vision research makes significant progress in tasks of understanding the global scene (image classification), and dense scene (semantic segmentation), our work takes steps to explore the gap in between. In this paper, we propose Multi-class Probability Pyramid as a representation built on the top of pixel-level semantic scene labels. This representation forms region-level abstractions by controlling the granularity of local semantic information, and thus disentangles the variation of scene semantics at different resolutions. We further show how such representation can be effectively used for evaluation purposes, including interpretable evaluation of scene segmentation and unsupervised diagnosis of segmentation predictions.